This repository contains code for building a forecasting model using Recurrent Neural Networks (RNNs). A climate-related dataset has been utilized for this experiment.

• Introduction
• Learning Objectives
• Usage
• Contributing
• License

Forecasting models play a crucial role in various domains, including climate science. In this project, we explore the implementation of RNNs, specifically SimpleRNN, LSTM, and GRU (Gated Recurrent Unit), for temperature forecasting using time series data.

At the end of the experiment, you will be able to:

• Understand and preprocess time series data
• Implement different types of Recurrent Neural Networks - SimpleRNN, LSTM, GRU
• Build and train RNNs for temperature forecasting
• Gain insights into advanced RNN concepts such as recurrent dropout, stacking recurrent layers, and Bi-directional RNNs

To run the notebook locally on your machine, follow these steps:

1. Clone this repository:

   git clone https://github.com/Praveen76/Build-a-Forecasting-Model-using-RNN.git
   

2. Navigate to the repository directory:

   cd Build-a-Forecasting-Model-using-RNN
   

3. Open the notebook file Build_a_Forecasting_Model_using_RNN.ipynb using Jupyter Notebook or JupyterLab.

4. Ensure you have all the required dependencies installed. You can install them using the following command:

   pip install -r requirements.txt
   

5. Execute the notebook cells one by one to understand the time series data, implement different RNN architectures, and build the forecasting model.

You can also run the notebook directly in your browser using Google Colab. Click the "Open in Colab" badge above, and it will open the notebook in Colab. Follow the instructions in the notebook to execute the cells and build the forecasting model.

Contributions are welcome! If you find any bugs or have suggestions for improvement, feel free to open an issue or create a pull request.

This project is licensed under the MIT License.

• This project is created and maintained by Praveen Kumar.

If you encounter any issues or have suggestions for improvement, please open an issue in the Issues section of this repository.

The code has been tested on Windows system. It should work well on other distributions but has not yet been tested. In case of any issue with installation or otherwise, please contact me on Linkedin

Happy coding!!

I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.